# MULLS: Versatile LiDAR SLAM via Multi-metric Linear Least Square

arXiv: https://arxiv.org/abs/2102.03771 · HTML: https://mlsanity.com/paper/2102.03771/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 68 references that are in the map.
- First posted on arXiv 7 Feb 2021.

## Builds on (most cited first, 27 of 27)

- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator](https://mlsanity.com/paper/1708.03852/) (2017)
- [PREDATOR: Registration of 3D Point Clouds with Low Overlap](https://mlsanity.com/paper/2011.13005/) (2020)
- [TEASER: Fast and Certifiable Point Cloud Registration](https://mlsanity.com/paper/2001.07715/) (2020)
- [A General and Adaptive Robust Loss Function](https://mlsanity.com/paper/1701.03077/) (2017)
- [LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping](https://mlsanity.com/paper/2007.00258/) (2020)
- [Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration](https://mlsanity.com/paper/1605.03344/) (2016)
- [Graduated Non-Convexity for Robust Spatial Perception: From Non-Minimal Solvers to Global Outlier Rejection](https://mlsanity.com/paper/1909.08605/) (2019)
- [LO-Net: Deep Real-time Lidar Odometry](https://mlsanity.com/paper/1904.08242/) (2019)
- [SegMap: Segment-based mapping and localization using data-driven descriptors](https://mlsanity.com/paper/1909.12837/) (2019)
- [IMLS-SLAM: scan-to-model matching based on 3D data](https://mlsanity.com/paper/1802.08633/) (2018)
- [Intensity Scan Context: Coding Intensity and Geometry Relations for Loop Closure Detection](https://mlsanity.com/paper/2003.05656/) (2020)
- [Tightly Coupled 3D Lidar Inertial Odometry and Mapping](https://mlsanity.com/paper/1904.06993/) (2019)
- [Loam_livox: A fast, robust, high-precision LiDAR odometry and mapping package for LiDARs of small FoV](https://mlsanity.com/paper/1909.06700/) (2019)
- [Exploiting Sparse Semantic HD Maps for Self-Driving Vehicle Localization](https://mlsanity.com/paper/1908.03274/) (2019)
- [LIMO: Lidar-Monocular Visual Odometry](https://mlsanity.com/paper/1807.07524/) (2018)
- [LIC-Fusion: LiDAR-Inertial-Camera Odometry](https://mlsanity.com/paper/1909.04102/) (2019)
- [LAMP: Large-Scale Autonomous Mapping and Positioning for Exploration of Perceptually-Degraded Subterranean Environments](https://mlsanity.com/paper/2003.01744/) (2020)
- [LodoNet: A Deep Neural Network with 2D Keypoint Matchingfor 3D LiDAR Odometry Estimation](https://mlsanity.com/paper/2009.00164/) (2020)
- [SLOAM: Semantic Lidar Odometry and Mapping for Forest Inventory](https://mlsanity.com/paper/1912.12726/) (2019)
- [Practical optimal registration of terrestrial LiDAR scan pairs](https://mlsanity.com/paper/1811.09962/) (2018)
- [LOCUS: A Multi-Sensor Lidar-Centric Solution for High-Precision Odometry and 3D Mapping in Real-Time](https://mlsanity.com/paper/2012.14447/) (2020)
- [Adaptive Robust Kernels for Non-Linear Least Squares Problems](https://mlsanity.com/paper/2004.14938/) (2020)
- [CAE-LO: LiDAR Odometry Leveraging Fully Unsupervised Convolutional Auto-Encoder for Interest Point Detection and Feature Description](https://mlsanity.com/paper/2001.01354/) (2020)
- [A Robust Laser-Inertial Odometry and Mapping Method for Large-Scale Highway Environments](https://mlsanity.com/paper/2009.02622/) (2020)
- [Sensor Aware Lidar Odometry](https://mlsanity.com/paper/1907.09167/) (2019)
- [PSF-LO: Parameterized Semantic Features Based Lidar Odometry](https://mlsanity.com/paper/2010.13355/) (2020)

## Built on by (most cited first, 27 of 27)

- [How NeRFs and 3D Gaussian Splatting are Reshaping SLAM: a Survey](https://mlsanity.com/paper/2402.13255/) (2024)
- [Behind the Curtain: Learning Occluded Shapes for 3D Object Detection](https://mlsanity.com/paper/2112.02205/) (2021)
- [Patchwork++: Fast and Robust Ground Segmentation Solving Partial Under-Segmentation Using 3D Point Cloud](https://mlsanity.com/paper/2207.11919/) (2022)
- [PIN-SLAM: LiDAR SLAM Using a Point-Based Implicit Neural Representation for Achieving Global Map Consistency](https://mlsanity.com/paper/2401.09101/) (2024)
- [CT-ICP: Real-time Elastic LiDAR Odometry with Loop Closure](https://mlsanity.com/paper/2109.12979/) (2021)
- [LODE: Locally Conditioned Eikonal Implicit Scene Completion from Sparse LiDAR](https://mlsanity.com/paper/2302.14052/) (2023)
- [Learning-based Multi-View Stereo: A Survey](https://mlsanity.com/paper/2408.15235/) (2024)
- [Consistent Direct Time-of-Flight Video Depth Super-Resolution](https://mlsanity.com/paper/2211.08658/) (2022)
- [ImMesh: An Immediate LiDAR Localization and Meshing Framework](https://mlsanity.com/paper/2301.05206/) (2023)
- [Unsupervised confidence for LiDAR depth maps and applications](https://mlsanity.com/paper/2210.03118/) (2022)
- [PINGS: Gaussian Splatting Meets Distance Fields within a Point-Based Implicit Neural Map](https://mlsanity.com/paper/2502.05752/) (2025)
- [Quadric Representations for LiDAR Odometry, Mapping and Localization](https://mlsanity.com/paper/2304.14190/) (2023)
- [Register Any Point: Scaling 3D Point Cloud Registration by Flow Matching](https://mlsanity.com/paper/2512.01850/) (2025)
- [Traj-LO: In Defense of LiDAR-Only Odometry Using an Effective Continuous-Time Trajectory](https://mlsanity.com/paper/2309.13842/) (2023)
- [How to Build a Curb Dataset with LiDAR Data for Autonomous Driving](https://mlsanity.com/paper/2110.03968/) (2021)
- [Colmap-PCD: An Open-source Tool for Fine Image-to-point cloud Registration](https://mlsanity.com/paper/2310.05504/) (2023)
- [MAD-ICP: It Is All About Matching Data -- Robust and Informed LiDAR Odometry](https://mlsanity.com/paper/2405.05828/) (2024)
- [IBISCape: A Simulated Benchmark for multi-modal SLAM Systems Evaluation in Large-scale Dynamic Environments](https://mlsanity.com/paper/2206.13455/) (2022)
- [GAM-Depth: Self-Supervised Indoor Depth Estimation Leveraging a Gradient-Aware Mask and Semantic Constraints](https://mlsanity.com/paper/2402.14354/) (2024)
- [Tightly-Coupled LiDAR-Visual SLAM Based on Geometric Features for Mobile Agents](https://mlsanity.com/paper/2307.07763/) (2023)
- [Multi-LiDAR Localization and Mapping Pipeline for Urban Autonomous Driving](https://mlsanity.com/paper/2311.01823/) (2023)
- [DeepPointMap: Advancing LiDAR SLAM with Unified Neural Descriptors](https://mlsanity.com/paper/2312.02684/) (2023)
- [PaGO-LOAM: Robust Ground-Optimized LiDAR Odometry](https://mlsanity.com/paper/2206.00266/) (2022)
- [WHU-Synthetic: A Synthetic Perception Dataset for 3-D Multitask Model Research](https://mlsanity.com/paper/2402.19059/) (2024)
- [ADA-DPM: A Neural Descriptors-based Adaptive Noise Filtering Strategy for SLAM](https://mlsanity.com/paper/2506.18016/) (2025)
- [GroundLoc: Efficient Large-Scale Outdoor LiDAR-Only Localization](https://mlsanity.com/paper/2510.24623/) (2025)
- [Rascene: High-Fidelity 3D Scene Imaging with mmWave Communication Signals](https://mlsanity.com/paper/2604.02603/) (2026)

## Abstract (arXiv)

The rapid development of autonomous driving and mobile mapping calls for off-the-shelf LiDAR SLAM solutions that are adaptive to LiDARs of different specifications on various complex scenarios. To this end, we propose MULLS, an efficient, low-drift, and versatile 3D LiDAR SLAM system. For the front-end, roughly classified feature points (ground, facade, pillar, beam, etc.) are extracted from each frame using dual-threshold ground filtering and principal components analysis. Then the registration between the current frame and the local submap is accomplished efficiently by the proposed multi-metric linear least square iterative closest point algorithm. Point-to-point (plane, line) error metrics within each point class are jointly optimized with a linear approximation to estimate the ego-motion. Static feature points of the registered frame are appended into the local map to keep it updated. For the back-end, hierarchical pose graph optimization is conducted among regularly stored history submaps to reduce the drift resulting from dead reckoning. Extensive experiments are carried out on three datasets with more than 100,000 frames collected by seven types of LiDAR on various outdoor and indoor scenarios. On the KITTI benchmark, MULLS ranks among the top LiDAR-only SLAM systems with real-time performance.